Automatic Fruits Identification System Using Hybrid Technique

Aibinu, Abiodun Musa; Salami, Momoh Jimoh Eyiomika; Shafie, Amir Akramin; Hazali, Norazlanshah; Termidzi, N.

doi:10.1109/delta.2011.47

Cited by 10 publications

(4 citation statements)

References 20 publications

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“…Then, fruit shape recognition and color recognition paths are combined for identifying and sorting fruits purposes. This approached is evaluated using apple, banana and mango images and results achieved accuracy of 99.1% [7].…”

Section: Related Workmentioning

confidence: 99%

Automatic Fruit Image Recognition System Based on Shape and Color Features

Zawbaa

Abbass

Hazman

et al. 2014

Communications in Computer and Information Science

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Abstract. This paper presents an automatic fruit recognition system for classifying and identifying fruit types. The work exploits the fruit shape and color, to identify each image feature. The proposed system includes three phases namely: pre-processing, feature extraction, and classification phases. In the pre-processing phase, fruit images are resized to 90 x 90 pixels in order to reduce their color index. In feature extraction phase, the proposed system uses scale invariant feature transform (SIFT) and shape and color features to generate a feature vector for each image in the dataset. For classification phase, the proposed model applies K-Nearest Neighborhood (K-NN) algorithm classification, and support vector machine (SVM) algorithm of different kinds of fruits. A series of experiments were carried out using the proposed model on a dataset of 178 fruit images. The results of carrying out these experiments demonstrate that the proposed approach is capable of automatically recognize the fruit name with a high degree of accuracy.

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Section: Related Workmentioning

confidence: 99%

Automatic Fruit Image Recognition System Based on Shape and Color Features

Zawbaa

Abbass

Hazman

et al. 2014

Communications in Computer and Information Science

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“…(4) An image processing based hybrid algorithm has been implemented for automatic identification and classification of fruits. The hybrid method relies on the techniques of Fourier descriptors (FD), spatial domain analysis (SDA) and artificial neural network (ANN) [5].…”

Section: Related Workmentioning

confidence: 99%

Image Analysis for Mango Fruit Defect Identification and Maturity Detection

Vishwanath¹

2023

Preprint

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The image processing and computer vision systems have been widely used for identification, classification, grading andquality evaluation in the agriculture area. Defect identification and maturity detection of mango fruits are challenging task for thecomputer vision to achieve near human levels of recognition. The proposed framework is useful in the supermarkets and can beutilized in computer vision for the automatic sorting of fruits from a set, consisting of different kind of fruits. The objective of thiswork is to develop an automated tool, which can be capable of identifying defect and detect maturity of mango fruits based on shape,size and color features by digital image analysis. MATLAB have been used as the programming tool for identification andclassification of fruits using Image Processing toolbox. Proposed method can be used to detect the visible defects, stems, size andshape of mangos, and to grade the mango in high speed and precision.

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“…Gila d.et.al [11] proposed a hybrid technique of automatic fruits identification system as a combination of ANN (Artificial Neural Network), FD (Fourier descriptors) and SDA (Spatial domain analysis) for developing the automatic fruits identification and sorting. They capture fruit images using digital camera and segment the captured images into shape, color and dielectric based.…”

Section: Literature Surveymentioning

confidence: 99%

Comparative Analysis of Various Soft Computing Techniques for Classification of Fruits

Singh¹,

Khehra²

2017

IJCA

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Fruit classification and recognition techniques play a significant role in various fruit based computer vision systems. Many techniques have been proposed so far to classify and recognize fruit images. However, these techniques not provide efficient results whenever fruit images have poor visibility due to poor environmental conditions. The overall objective of this paper is to study and explore various techniques which have been designed so far to classify and recognize fruit images. Comparisons have been drawn between some well-known fruit classification and recognition techniques based upon certain features. It has been observed that soft computing based fruit classification and recognition techniques performs efficiently than existing techniques.

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Automatic Fruits Identification System Using Hybrid Technique

Cited by 10 publications

References 20 publications

Automatic Fruit Image Recognition System Based on Shape and Color Features

Automatic Fruit Image Recognition System Based on Shape and Color Features

Image Analysis for Mango Fruit Defect Identification and Maturity Detection

Comparative Analysis of Various Soft Computing Techniques for Classification of Fruits

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